209 research outputs found
[News Clip: Cockrell Hill]
No full textVideo footage from the WBAP-TV television station in Fort Worth, Texas, to accompany a news story about a food drive for the family of Robert A. Jones in Cockrell Hill
Cockrell Banana Co. Invoices
No full textMaterials documenting the Robert Atkinson rolling store: invoices from Cockrell Banana Co. to Robert Atkinson.https://scholarsjunction.msstate.edu/mss-atkinson-papers/1051/thumbnail.jp
Letter from Robert Jemison, Jr., Tuskaloosa, Alabama to Mr. Cockrell, Coalfire Mills, February 7, 1855
No full textRobert Cockrell, bass trombone
No full textRalph Vaughan WilliamsEdward GregsonRichard StraussClay Smith, arr. Blair Bollinge
NBC News Scripts
No full textScript from the WBAP-TV/NBC station in Fort Worth, Texas, covering a news story about a food drive for the family of Robert A. Jones in Cockrell Hill
Letter and bill from S. W. Cockrell, Eutaw, Alabama, to Henry A. Snow, Tuscaloosa, Alabama, January 19, 1857
No full textThis item is from the Robert Jemison, Jr. Papers. The collection spans the period from 1797 to 1960 and includes both the personal and business papers of Robert Jemison, Jr., along with papers of Robert Jemison (grandfather), William Jemison (father), Priscilla Jemison (wife), Cherokee Jemison Hargrove (daughter), and Andrew Coleman Hargrove (son-in-law), and Robert Jemison, Jr. (IV) of Birmingham (1878-1973). Included are the records of his grist and lumber mills, plantations, stage line, the Tuskaloosa Plank Road, toll bridges, ferries, postal contracts, and the North East and South West Railroad
Model-based machine learning to identify clinical relevance in a high-resolution simulation of sepsis and trauma
No full textIntroduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning techniques are a promising option to augment the model development process such that parameterization and calibration are performed intelligently and efficiently. Methods: We used the Keras framework to train an Artificial Neural Network (ANN) for the purpose of identifying critical biological tipping points at which an in silico patient would heal naturally or require intervention in the Innate Immune Response Agent-Based Model (IIRABM). This ANN, determines simulated patient “survival” from cytokine state based on their overall resilience and the pathogenicity of any active infections experienced by the patient, defined by microbial invasiveness, toxigenesis, and environmental toxicity. These tipping points were gathered from previously generated datasets of simulated sweeps of the 4 IIRABM initializing parameters. Results: Using mean squared error as our loss function, we report an accuracy of greater than 85% with inclusion of 20% of the training set. This accuracy was independently validated on withheld runs. We note that there is some amount of error that is inherent to this process as the determination of the tipping points is a computation which converges monotonically to the true value as a function of the number of stochastic replicates used to determine the point. Conclusion: Our method of regression of these critical points represents an alternative to traditional parameter-sweeping or sensitivity analysis techniques. Essentially, the ANN computes the boundaries of the clinically relevant space as a function of the model’s parameterization, eliminating the need for a brute-force exploration of model parameter space. In doing so, we demonstrate the successful development of this ANN which will allows for an efficient exploration of model parameter space
Funeral Services for Mrs. Freddie Lee Cockrell
No full textFuneral program for Mrs. Freddie Lee Cockrell, born January 15, 1915. The funeral was held January 25, 1986 at Calvary Baptist Church, officiated by Rev. Robert Miller, Jr. The funeral arrangements were made through Lewis Funeral Home and she was buried in Southern Memorial Park in San Antonio, Texas
Will Cockrell on Everest, Inc.
No full textWill Cockrell is the author of the book Everest, Inc.: The Renegades and Rogues Who Built an Industry at the Top of the World, a fascinating history of the guiding industry on the world’s highest peak.
In this episode, Justin and Will discuss how his book challenges misconceptions about Everest, the evolving role of Sherpas, and why this mountain holds such a firm grip on our psyche.https://scholarworks.umt.edu/anewangle_podcasts/1342/thumbnail.jp
The Burn Agent-Based Model (BABM): Developing a Computational Agent-based Model of the Burn Wound Healing Process to Examine Fibrosis and Re-epithelialization After a Partial-thickness Burn Injury
No full textIntroduction: Partial-thickness burn injuries, depending on depth, are distinct from deeper burns in that they can preserve the hair follicle bulb as a source of epithelial stem cells and a modulator of mechanical forces and immune cells. This changes the dynamics of wound healing and is an important consideration for therapeutic decisions. Regardless, patients require appropriate interventions to facilitate proper restoration of the skin to prevent complications like wound infection or sepsis that can occur as sequelae of the disrupted epithelial barrier and systemic inflammatory cascades. Herein, we developed a computational agent-based model of the partial-thickness burn wound environment, the Burn Agent-based Model (BABM), in order to better characterize the complex systems of wound healing and facilitate investigation of novel therapeutic strategies. Methods: The model was developed using NetLogo and simulates a two-dimensional cross-section of epidermis and dermis containing multiple hair follicles. It includes multiple cell types present in those layers, such as keratinocytes, epithelial stem cells, neutrophils, macrophages, and fibroblasts, as well as key cytokines and chemokines. Burn injuries were implemented as a mechanical “shearing” of the entire epidermis and the superficial portion of the dermis with damage to the immediately deeper tissue. The model encompasses the first three weeks of the post-burn period, including hemostasis and coagulation, inflammation, proliferation, and re-epithelialization. Results: We successfully created an ABM of the partial-thickness burn wound healing process at the cellular and cytokinetic level. The model implements the mitotic function of epithelial stem cells particularly within hair follicle bulbs, paracrine influences of injured keratinocytes, phagocytic and inflammatory actions of neutrophils and macrophages, migration and extra-cellular matrix deposition by fibroblasts, and the vascular dynamics of dermal capillaries representing the zones of coagulation, stasis, and hyperemia. In addition, the model incorporates multiple cytokines, notably IL1, IL6, TNFa, TGFb, EGF, FGF, PDGF, and VEGF. The model was calibrated to closely follow the cytokine profiles obtained from cytokine multiplex assays of patients hospitalized for burn injuries. Conclusion: The BABM provides a powerful in-silico framework to study burn wound healing by simulating a small segment of injured skin that can be scaled near infinitely to represent a wound of any size but does not include the wound edge. As the model is refined and further validated, our goal is to allow for model calibration with individualized patient data in order to create a personalized “digital twin” that closely mimics the patient’s physiology and may augment diagnostic or therapeutic decision-making in the clinical setting
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